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近年來，快速葉綠素熒光誘導動力學曲線(OJIP曲線)及其數(shù)據(jù)分析方法JIP-test在植物科學研究中的應用越來越廣泛(BussottiF, et al., 2020; KalajiH M, et al., 2017; Pontes. D, 2019; Tsimilli-michael M, 2020)。OJIP曲線可以更直觀地表現(xiàn)出差異，JIP-test則提供豐富的參數(shù)，由于其測定方便簡單，逐漸成為科研工作者們研究光合作用原初光化學反應的有力工具。

JIP-test提供豐富的參數(shù)，PCA進行數(shù)據(jù)降維處理，兩者結合，能夠快速處理并分析大量的實驗數(shù)據(jù)，(i)揭示影響實驗的主要參數(shù)，并可(ii)聚類不同處理之間的差異，也可用于(iii)大數(shù)據(jù)分析并預測植物生長變化。下面通過三篇文章來詳細介紹二者的結合應用。

近年來，全球范圍內(nèi)短期內(nèi)澇等自然災害頻發(fā)，并且隨著北半球高緯度地區(qū)秋冬季降水量的增加，這種情況的出現(xiàn)可能會更加頻繁。研究結果表明，淹水溫度是影響植物對該脅迫反應的重要因素。該試驗研究了耐寒性不同的四種高羊茅在低溫下對土壤水分過剩的光合機構響應，旨在驗證Rubisco活性改變引起的葉片水溶性碳水化合物濃度變化是否會影響土壤水分過剩條件下的光適應。

圖1. 主成分分析的向量圖，顯示了低溫對照和低溫淹水被調(diào)查變量之間的相關性

光是控制植物生長發(fā)育的主要因素。光不僅推動植物光合作用，光質(zhì)和光照時間還驅(qū)動著植物主要的發(fā)育變化，如光形態(tài)發(fā)生、開花的光周期誘導、向光性、避蔭以及防御等。為了評估光照條件對植物生理狀態(tài)的影響，該研究在豌豆植株的早期發(fā)育過程中使用正常白光(W)、白色陰影(WS)、高光強藍/紅/遠紅組合光(BR)和低光強藍/紅/遠紅組合光(BRS)四種光照射，采用JIP-test來評估與光吸收和電子傳輸有關的PSII參數(shù)，并通過PCA技術聚類分析不同光照之間的差異。

圖2. JIP-test參數(shù)和不同處理的主成分分析(Plant variants: W – white light; WS – white light with shadow; BR – blue and red light; BRS – blue and red light with shadow)

在大規(guī)模生態(tài)調(diào)查中，為了達成篩選目的和效率，一般使用有限的參數(shù)來對樣本進行快速、簡單的評價和生理分類。人們提出了許多形態(tài)、化學和生理指標來評價生態(tài)系統(tǒng)中的植物狀況。其中，葉綠素的瞬時熒光分析(JIP-test)被認為特別適用于大型生態(tài)調(diào)查，并能在短時間內(nèi)篩選出許多樣品。

本研究中分析的最大數(shù)據(jù)集源自FunDivEUROPE項目(Functional Significance ofthe Forest Diversity in Europe, European Union, 7th FrameworkProgram)。此項目中分析了整個歐洲的森林生態(tài)系統(tǒng)，從地中海到歐洲北部地區(qū)，涵蓋了豐富的差異樹種組成。其中包括天然高大森林(In Italy, Spain, Romania,Poland,Finland, Baeten et al., 2013)和人工林場(In Finland and Germany, seeVerheyen et al.,2016)。

如何實現(xiàn)對葉綠素a熒光數(shù)據(jù)(JIP-test參數(shù))、其它生理參數(shù)和基因、蛋白等分子數(shù)據(jù)組成的大數(shù)據(jù)庫進行PCA分析？

通常我們可以使用學術界常用的商用數(shù)據(jù)分析軟件進行PCA分析，如SPSS Statistics(IBM Corp)、Statistica(StatSoft Inc. 2011)和SAS(SAS Enterprise Miner; SAS Institute, Cary, NC)等。

在全球互聯(lián)網(wǎng)化的大趨勢下，也涌現(xiàn)出一批使用體驗更佳、分析更加智能化的在線數(shù)據(jù)分析工具，如SPSSAU(QingSi Technology Ltd 2016-2020)、ClustVis(Metsalu, Tauno et al. 2015)等。

此外以R語言和Python為代表的計算機程序設計語言可以實現(xiàn)對大數(shù)據(jù)的快速智能處理、計算和制圖，使用R語言和Python對JIP-test熒光數(shù)據(jù)進行PCA數(shù)據(jù)分析也已有非常成熟的語言包進行應用。

下期文章我們將以IBM SPSS Statistics 26為例詳細介紹JIP-test熒光參數(shù)PCA分析操作方法，敬請期待！

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